Evidence map›Paper›PMID 38328293›Full record

ArticleData in brief2024

Image dataset of healthy and infected fig leaves with Ficus leaf worm.

Saad Jabir Hafi, Mohammed Abdallazez Mohammed, Dhafar Hamed Abd, Haya Alaskar, Nawaf R Alharbe, Sam Ansari, Salah A Aliesawi, Abir Jaafar Hussain

Abstract read
In one paragraph

Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Dataset ofData in brief · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Saad Jabir HafiUniversity of Anbar, Upper Euphrates Basin Developing Center, Iraq.
Mohammed Abdallazez MohammedUniversity of Karbala, College of Computer Science and Information Technology, Department of Computer Science, Iraq.
Dhafar Hamed AbdCollege of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq.
Haya AlaskarComputer Science Department, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
Nawaf R AlharbeCollege of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia.
Sam AnsariDepartment of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates.
Salah A AliesawiCollege of Computer Science and Information Technology, University of Anbar, Ramadi, Iraq.
Abir Jaafar HussainDepartment of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work presents an extensive dataset comprising images meticulously obtained from diverse geographic locations within Iraq, depicting both healthy and infected fig leaves affected by Ficus leafworm. This particular pest poses a significant threat to economic interests, as its infestations often lead to the defoliation of trees, resulting in reduced fruit production. The dataset comprises two distinct classes: infected and healthy, with the acquisition of images executed with precision during the fruiting season, employing state-of-the-art high-resolution equipment, as detailed in the specifications table. In total, the dataset encompasses a substantial 2,321 images, with 1,350 representing infected leaves and 971 depicting healthy ones. The images were acquired through a random sampling approach, ensuring a harmonious blend of balance and diversity across data emanating from distinct fig trees. The proposed dataset carries substantial potential for impact and utility, featuring essential attributes such as the binary classification of infected and healthy leaves. The presented dataset holds the potential to be a valuable resource for the pest control industry within the domains of agriculture and food production.

Indexed as

ClassificationComputer visionDeep learningFicus Leaf WormFig treeMachine learningPattern recognition

Identifiers

PMID38328293
PMCPMC10847847

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.